Studentship Vacancies

PhD Studentship Vacancies

The following list of topics, grouped by research area, are available this year.

Area 1. Blockchains and Decentralised Systems

A blockchain is an open, decentralised and trustable system. Without the need for any central control or mediator, blockchains allow us to rethink applications in a decentralised way, providing a provenance protocol for sharing data across disparate semi-trusting organisations.

  • Self-sovereign Valuable Personal Data

    The over centralisation of data has led Tim Berners-Lee to call the Web 'anti-human'. This PhD will investigate how combinations of decentralised data and identity technologies (e.g. self-sovereign identity, Solid personal data pods) can be used to give users control of their own data whilst ensuring that others can still trust its contents, where it might otherwise be valuable to users to edit it. Educational qualifications are one example of this; applications could also include health research, and equitable access to data infrastructure and value for marginalised groups. This PhD will build on existing work where we have created a framework (LinkChains) for handling personal and sensitive data which combines decentralised data platforms with blockchain-based verification.

    Supervisors: Dr Aisling Third and Prof John Domingue

    Keywords: Identity Self-sovereignty Blockchain Decentralised Ledgers Linked Data Knowledge Graph Solid EDI

    Skillset: Software and Web development Blockchain (especially Ethereum) Linked Data

  • Blockchain for Education

    This PhD project will investigate the potential of how Blockchain technology can transform education. For example supporting new forms of accreditation such as micro-credentials, Open Badges, reputation validation, ePortfolios, self-sovereignty for student data and radically new types of universities. Blockchain is the technology underlying crypto-currencies like Bitcoin, offering a publicly shared immutable ledger that can be used in many interesting and potentially revolutionary scenarios in education. For more information on this topic, take a look at this badged open course.

    Supervisors: Dr Alexander Mikroyannidis and Prof John Domingue

    Keywords: Blockchain Accreditation Micro-credentials Open Badges ePortfolios

    Skillset: Software development Education skills/interest Blockchain skills/interest

  • Decentralised Online Media Commenting for 21st Century Digital Journalism

    Media and News companies (The Guardian, The Independent, New Your Times, The Telegraph, etc) are increasingly shutting down online commenting sections. This is mostly due to the lack of existing discussion platforms that can effectively respond to the requirements of civil, accountable discussion that a media company have to guarantee to their readers, which includes issues of anonymity and authenticity of people, facts and opinions. This PhD aims to study, design and develop a novel Digital Journalism Discussion tool which goes beyond these limitations and offers a viable alternative for media companies to re-open commenting to the wider community of readers. This technology will build on cutting edge AI and Decentralised system approaches to facilitate and monitor healthy discussions and provide mechanisms for users to stay anonymous but accountable. This way to manage online identity in media discussion will promote civilised yet free speech in online media commenting.

    Supervisors: Anna De Liddo

    Keywords: Online Media Commenting Digital Journalism Social Justice Anonymous Reputation Decentralised Systems

    Skillset: Web Development Python Distributed LedgersStatisticsUser Studies/Researcha passion for digital journalism

Area 2. Computational Social Science

Computational Social Science research targets the enhancement of critical societal issues through the use of Artificial Intelligence solutions. This research aligns with the core values of The Open University, and has contributed to urgent and vital topics, such as misinformation detection, online radicalisation and extremism, crisis management, and climate change.

  • Online misinformation

    Misinformation is compromising our ability to form informed opinions about various critical issues relating to politics, health, environment, and economy. This PhD can be focused on the use of computational methods to tackle any relevant topic, such as explainable AI detection methods, tracking the spread of misinformation across social media platforms, using knowledge graphs for misinformation processing, measuring and predicting their potential harm and impact, and evaluating the effectiveness of various proactive corrective measures on individuals and networks.

    Supervisors: Prof Harith Alani and Dr Gregoire Burel, Dr Tracie Farrell

    Keywords: Misinformation Social Media Data Science

    Skillset: Computer programming Machine Learning Social Network Analysis Large-Scale Data Analysis

  • Safeguarding Children Online

    Children are increasingly subjected to various threats in online media and gaming platforms, such as abuse, aggression, cyberbullying, grooming, and sextortion. Automatic identification and prevention of such threats are critical for safeguarding children online. For this PhD, candidates will investigate privacy-aware computational methods, explainable AI models, online behavioural analysis, and NLP approaches for tackling issues relating to online threats directed towards children. Areas of interest may include the identification of potentially harmful content directed at children, mental health risk analysis, and online grooming and abuse towards specific children groups, demographics and minorities.

    Supervisors: Prof Harith Alani and Dr Gregoire Burel, Dr Lara Piccolo

    Keywords: Children Online Abuse Mental Health Social Media Data Science

    Skillset: Computer programming NLP Machine Learning Social Network Analysis Large-Scale Data Analysis

  • Assessing and Mitigating the Impact of Global Phenomena, Geopolitical Factors, and Bias on Research

    The scientific enterprise is affected by global phenomena, such as the COVID-19 pandemic, geopolitical factors and different kinds of bias. This PhD project aims at shedding light on these issues and assessing their impact across gender, countries, disciplines and others. The main objective is to exploit large-scale datasets of scholarly knowledge, i.e., scientific knowledge graphs, to analyse collaboration, productivity, and other factors with the aim of understanding the extent of the phenomenon and identifying strategies to make scientific research more inclusive and resilient to external factors.

    Supervisors: Dr Angelo Salatino, Dr Francesco Osborne and Prof Enrico Motta

    Keywords: Scientometrics Scholarly Analytics Covid-19 Scholarly Data Semantic Web Science of Science Social Science Knowledge Graphs

    Skillset: Interest/expertise in Science Interest/expertise in Ethics and Justice Data Science Computer Programming Network Science Data Integration

  • Intersectional Hateful Speech

    Hateful speech online is a threat to civil society, and the advancement of equality, diversity and inclusion. Many approaches to investigating hateful speech online, particularly when supported by computational approaches for automated detection, focus on either generally offensive and toxic language, or hate toward specific groups. Intersectional forms of hate that relate to more than one protected characteristic are more difficult to spot and tackle. In addition, the focus for computational approaches has been on detection and tracking, rather than understanding or mitigating the impacts of online harm. In this PhD, you are welcome to propose any topic that addresses intersectional hate on the web, and that incorporates computational approaches for understanding the problem or mitigating it.

    Supervisors: Prof Miriam Fernandez, Dr Tracie Farrell

    Keywords: Intersectionality Hate Speech Machine Learning Online Harm

    Skillset: Computer programming Machine Learning (desired) Social Network Analysis Qualitative Analysis AI for Equality) Diversity and Inclusion (AI4EDI)

  • Mental Health and Supportive FinTech

    Some mental health and cognitive conditions, such as BiPolar and Dementia, put individuals at a higher risk for poor financial outcomes. These can include predatory debt, unpaid bills and overspending, which can lead to poor credit rating and long-term debt. While financial institutions may offer some support, individuals can find it difficult to understand and accept the assistance that is being offered. Financial technology that could support people with BiPolar or Dementia would have to address the biggest issues for such individuals, before a crisis hits. In this PhD, we invite you to explore financial patterns that could be relevant to the conditions of BiPolar or Dementia, and how to automatically detect such patterns from financial transactions data. Project will be in collaboration with a leading financial payment scheme.

    Supervisors: Prof Harith Alani Dr Tracie Farrell Prof Miriam Fernandez, Dr Hasan Al-Madfai (external supervisor)

    Keywords: FinTech Mental Health Machine Learning Advance Choice Tools

    Skillset: Computer programming Machine Learning (desired) Qualitative Analysis

Area 3. Data Science and Extended Intelligence

Data Science and Extended Intelligence go beyond efficient data infrastructure and engineering, it studies data empowered human processes that lead to smarter, fairer, more sustainable and equitable ways of living.

  • Collective Intelligence Technologies for Social Tolerance

    The dialogue spaces that we see on the web today produce polarisation, division and conflict. Research evidence clearly indicates that people tend to select information from people who hold similar positions and support similar worldviews. On this account, social media companies have designed social media platforms to recommend content on the basis of similarity, popularity and agreement-only principles. This combination of homophily and lack of content variety has proved to degrade the quality, balance and safety of online discourse, up to undermining social tolerance. This PhD will focus on the development of new social media commenting systems that use hybrid AI-CI (AI-with human in the loop) methods to reduce polarisation, support serendipitous knowledge discovery and improve social tolerance in online media commenting.

    Supervisors: Anna De Liddo

    Keywords: Digital Journalism Social Justice

    Skillset: (required) Programming (Web Development, Python) Statistics Strong drive toward developing social impact

  • Hybrid Intelligence for Knowledge Graph Construction

    The project aims at designing novel methods for constructing Knowledge Graphs (KG) integrating data from heterogeneous sources, combining symbolic (rules, plans) and subsymbolic AI (machine/deep learning). The candidate will have a strong interest in RDF, SPARQL, and similar formalisms (OWL, SHACL) and will contribute to addressing issues in KG construction and application such as (1) automating KG generation from structured or unstructured data sources; (2) accessing non-RDF resources with SPARQL (Virtual Knowledge Graphs); (3) improving the usability of KG construction systems for non-expert users.

    Supervisors: Dr Enrico Daga and Dr Paul Mulholland

    Keywords: Data integration Semantic Web

    Skillset: (required) programming basics of AI Strong Interest in Semantic Web technologies

  • Artificial Intelligence Systems for Analysing the Scientific Literature

    This PhD project aims at developing a new generation of intelligent systems for supporting researchers in analysing and exploring the scientific literature, with the ultimate goal of improving efficiency and verifiability of research. Traditional approaches for searching the literature do not scale to the large number of articles produced today. It is thus crucial to introduce innovative AI-based solutions that can automatically extract and leverage machine-readable representations of research knowledge. The candidate will produce novel approaches based on NLP, semantic technologies, and deep learning for answering complex queries on the literature, recommending articles, predicting emerging topics, and producing research hypotheses.

    Supervisors: Dr Francesco Osborne, Dr Angelo Salatino and Prof Enrico Motta

    Keywords: Knowledge Graphs Science of Science Deep Learning Scholarly Data Scholarly Analytics Information Extraction

    Skillset: Artificial Intelligence Computer Programming Knowledge Graphs Computer Programming Interest/expertise in Research/Science

  • Understanding and Improving the Relationship between Academia, Industry, and Society

    The main objective of this project is to analyse the relation between academia, industry, and society with the aim of understanding how these stakeholders influence each other and producing novel solutions to improve their knowledge flow and accelerate scientific progress. In particular, we will focus on analysing how new ideas and technologies from academia are implemented in concrete products by industry and adopted by society. The student will integrate and leverage information from heterogeneous sources (e.g., scientific articles, patents, social media, news) and design AI-based analytical solutions to investigate the dynamics in this space and improve knowledge exchange.

    Supervisors: Dr Angelo Salatino, Dr Francesco Osborne and Prof Enrico Motta

    Keywords: Scientometrics Scholarly Analytics Scholarly Data Semantic Web Science of Science Social Science Knowledge Graphs

    Skillset: Data Science Computer Programming Data Mining Data Integration Visual Analytics

  • Human AI learning through dialogic interfaces

    This PhD research will explore novel collaborative learning environments proposed and appraised using Bakhtin's dialogical philosophy underlining the use of language (text and visual) and interactions to facilitate collaborative learning (human and machine learning) and meaning making [1,2]. The emphasis is on the design and evaluation of Human-AI collaborations which support group cognition for example by supporting meaning negotiation, knowledge building and dialogical interactions. Projects are welcomed for integrating 'dialogic AI participation' in small group collaborations leading to decision making, problem-solving and consensus building such as in wikis, social media technologies and citizen science platforms.

    [1]. Mikhail Bakhtin. 1984. Problems of Dostoevsky's poetics (C. Emerson, Trans. C. Emerson Ed.). University of Minnesota Press, Minneapolis.
    [2]. Stefan Trausan-Matu, Rupert Wegerif, and Louis Major. 2021. Dialogism. In International Handbook of Computer-Supported Collaborative Learning, Ulrike Cress, Carolyn Rosé, Alyssa Friend Wise and Jun Oshima (eds.). Springer International Publishing, Cham, 219-239.

    Supervisors: Dr Nirwan Sharma, Prof Advaith Siddharthan and Prof Stefan Rueger

    Keywords: Collaborative learning Human-Computer Interaction Dialogism Machine learning AI Citizen Science

    Skillset: Programming Machine Learning Mixed methods User Evaluations

  • Open Research Graph

    Research graphs, i.e. knowledge graphs representing research entities such as research papers, dataset, software, scientific methods, citations with their rhetorical function, institutions and research fields, are fundamental to the organisation of research knowledge and have seen a sharp increase in use across a wide range of application areas, including but not limited to search engines, recommender systems, innovation management, identifying scientific consensus and technology-assisted research assessment. As Microsoft decided to retire (in December 2021) its widely popular product Microsoft Academic Graph to refocus its team on building knowledge graphs for corporate documents, there is currently a gap and substantial demand for an Open Research Graph. This project aims to develop novel AI methods for knowledge graph generation. The student will work with the world's largest and continuously growing dataset of full text open access research papers, hosted by the research group at and which has over 30 million monthly active users. The student will be able to test the developed technology in production in a real-world use case in cooperation with several companies.

    Supervisors: Dr Petr Knoth, David Pride

    Keywords: Knowledge Graph Research Graph Machine Learning Artificial Intelligence Big Data Open Science Open Access

    Skillset: NLP Machine Learning Information RetrievalData Mining

  • Responsible use of AI in recommender systems for finding experts

    The objective of this project is to develop innovative AI methods for identifying experts possessing highly specific knowledge and skills. Funders look for reviewers of project proposals, publishers look for reviewers of research manuscripts, universities look internally and externally for people with relevant skills who understand and can solve their research challenges, companies look for consultants who can help them solve real-world problems. The process of finding experts with the necessary skills is time and resource intensive and can be made significantly more efficient with the use of AI / semantic / and information retrieval technologies. In addition to efficiencies, technological solutions can bring about benefits in countering bias, inequality and discrimination in a space that is currently strongly lead by 'who you know'. The student will work with the world's largest and continuously growing dataset of full text open access research papers, hosted by the research group at and which has over 30 million monthly active users. The student will be able to test the developed technology in production in a real-world use case with scientific publishers / funders.

    Supervisors: Dr Petr Knoth, David Pride

    Keywords: Knowledge Graph Research Graph Machine learning AI Big Data Open Science Open Access

    Skillset: NLP Machine Learning Information Retrieval Data Mining Big Data

Area 4. New Media in Society

New Media and Society research aims at going beyond the study of Computing and ICT from a technology perspective, and looks at improving our understating human values and the impact of technology innovations on people's lives and their communities. This research particularly looks at ways to use new media to promote social justice and tackle complex societal challenges of inclusion and disadvantage.

  • Improving Citizen Participation in the Climate Debate with Multimedia Deliberation Technologies

    The public is increasingly worried about climate change, especially young generations are concerned about their future on this planet and seek new ways to be engaged in a more democratic inclusive discussion about climate action. Still, technology for citizen engagement and online deliberation of climate change issues are either very simple (such as social media) but inappropriate to support healthy and effective deliberation, or too complex (such as as Kialo, Deliberatorium, DebateGraph, The Evidence Hub), and therefore, hardly inclusive and adopted at scale. This PhD research aims at improving the accessibility of large-scale online discussion platforms by providing novel features for voice-to-text translation and multimedia contribution to the online debate (in form of audio and video file). This will enable participants to use oral and gestural communication to engage with climate debates, thus promoting participation from younger and/or less digitally savvy communities (which usually are less at ease with written communication).

    Supervisors: Anna De Liddo, Dr Lara Piccolo

    Keywords: Online Deliberation Climate Change Large Scale Argumentation Multimedia Interaction voice-to-text systems

    Skillset: (required) Programming (Web Development, Python) User Studies/Research, Statistics

  • Citizen Curation

    Many museums have a participation gap: the collections and exhibitions predominantly attract physical and virtual visitors that are white and from higher socioeconomic groups. The problem is not cost, rather many people perceive a lack of relevance in what is offered by cultural institutions. Citizen Curation has been proposed as a solution, in which the public are supported in developing and sharing their own interpretations and responses to cultural works, widening the range of voices presented in the museum. This PhD will draw on work in HCI and Digital Humanities to develop ways in which the public can be supported in developing and sharing their responses to artworks, and the museum can be supported in managing those contributions and using them as part of their public offering.

    Supervisors: Dr Paul Mulholland and Dr Enrico Daga

    Keywords: Citizen Curation Museums Participation gap

    Skillset: HCI Web Programming Digital Humanities Interest in Cultural Engagement

  • Human interaction with musical content and data

    An increasing amount of data can be derived about the music we listen to, including information about artists, the musical features of the piece, where the song has been performed, how it was made and how it was received by critics and the public. Much of this data is disconnected from the music listening experience provided by streaming services and other listening platforms. This PhD will research and develop ways in which music data can be used to enrich the music listening experience, helping people to learn more about their musical tastes, discover new music and share and compare their listening experiences with friends. The work will be conducted at the intersection of HCI and Artificial Intelligence/Knowledge Graphs, understanding how AI-derived musical data can be made usable and useful for the general music listener.

    Supervisors: Dr Paul Mulholland and Dr Enrico Daga

    Keywords: Music Musical Data Music Interaction

    Skillset: HCI Web Development AI Knowledge Graphs Interest in Music Interaction

  • Artificial Intelligence Systems for News Analysis and Media Agenda Monitoring

    This project aims to design novel AI techniques for multi-dimensional understanding and analysis of news. To this purpose, the candidate will develop novel approaches that will combine machine learning and natural language processing techniques for extracting a comprehensive semantic description of news content to support journalistic competence. The resulting knowledge graphs will enable the representation and reasoning over news frames to perform comparative analyses of media outlets, including viewpoints and biases. The candidate will focus on tasks such as news classification, prediction of news topics, monitoring event coverage, and agenda-setting analysis.

    Supervisors: Dr Enrico Daga, Dr Francesco Osborne and Prof Enrico Motta

    Keywords: Data Science News Analysis Deep Learning Knowledge Graphs, Natural Language Processing Information Extraction

    Skillset: Computer programming Information extraction Knowledge Graphs Interest/Expertise in News and Media

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